Chaque enregistrement correpond à un patient qui a réalisé une ponction lombère (LCR). Une LCR n’étant pas réalisée pour des raisons mineurs, tous les patients de la BDD ont présentés des problèmes cognitifs.
L’objectif de l’étude est d’analyser l’association entre différents biomarqueurs sanguins et les protéines Abeta et tau, marqueurs de la maladie d’Alzheimer.
## [1] "Nombre d'observations : 1723"
## [1] "Nombre de variables : 176"
## $breaks
## [1] 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
##
## $counts
## [1] 1 0 1 2 3 12 38 104 163 226 369 335 272 155 40 2
##
## $density
## [1] 0.0001160766 0.0000000000 0.0001160766 0.0002321532 0.0003482298
## [6] 0.0013929193 0.0044109112 0.0120719675 0.0189204875 0.0262333140
## [11] 0.0428322693 0.0388856645 0.0315728381 0.0179918746 0.0046430644
## [16] 0.0002321532
##
## $mids
## [1] 17.5 22.5 27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5
## [16] 92.5
##
## $xname
## [1] "d"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
1 = non scolarisé
2 = primaire sans diplôme
3 = certificat d’études
4 = secondaire (collège/CAP/brevet)
5 = secondaire (BEP/lycée)
6 = baccalauréat
7 = études supérieures
8 = non renseigné
## [1] "Jamais marié" "Divorcé" "Marié / en couple"
## [4] "Veuf"
## [1] "Insuffisance pondérale" "Corpulence normale"
## [3] "Obésité modérée" "Obésité morbide ou massive"
## [5] "Obésité sévère" "Surpoids"
data2 = clean_names(data2)
L = c("trt1","trt2","trt3","trt4","trt5","trt6","trt7","trt8","trt9","trt10",
"trt11","trt12","trt13","trt14","trt15","trt16","trt17","trt18","trt19","trt20")
trt_total = data.frame(x = data2$trt1)
for (i in 2:length(L)){
b = data.frame(x = data2[,L[i]])
trt_total = rbind(trt_total,b)
}
trt_total = trt_total %>%
mutate(x = tolower(x))%>%
filter(x != c(""))
### Algo
for (i in 1:nrow(trt_total)){
trt_total[i,1] = gsub("y","i", trt_total[i,1])
trt_total[i,1] = gsub("ll","l", trt_total[i,1])
trt_total[i,1] = gsub("ff","f", trt_total[i,1])
trt_total[i,1] = gsub("ph","f", trt_total[i,1])
trt_total[i,1] = gsub("h","", trt_total[i,1])
trt_total[i,1] = gsub("pp","p", trt_total[i,1])
trt_total[i,1] = gsub(",","", trt_total[i,1])
trt_total[i,1] = gsub("_","", trt_total[i,1])
#trt_total[i,1] = gsub(".","", trt_total[i,1])
trt_total[i,1] = gsub("0","", trt_total[i,1])
trt_total[i,1] = gsub("1","", trt_total[i,1])
trt_total[i,1] = gsub("2","", trt_total[i,1])
trt_total[i,1] = gsub("3","", trt_total[i,1])
trt_total[i,1] = gsub("4","", trt_total[i,1])
trt_total[i,1] = gsub("5","", trt_total[i,1])
trt_total[i,1] = gsub("6","", trt_total[i,1])
trt_total[i,1] = gsub("7","", trt_total[i,1])
trt_total[i,1] = gsub("8","", trt_total[i,1])
trt_total[i,1] = gsub("9","", trt_total[i,1])
if (length(grep(" ", trt_total[i,1], ignore.case = TRUE)) > 0){
pos = regexpr(pattern=" ",trt_total[i,1],fixed=TRUE)[1]
trt_total[i,1] = substr(trt_total[i,1],1,pos-1)
}
trt_total[i,1] = gsub(" ","", trt_total[i,1])
if(substr(trt_total[i,1],nchar(trt_total[i,1]),nchar(trt_total[i,1])) == "e"){
trt_total[i,1] = gsub('.{1}$', '', trt_total[i,1])
}
}
medi_total = c(medi_chol,medi_diabete,medi_HTA,medi_antidouleur,medi_anxiete,medi_cv,medi_depression,medi_ma,medi_parkinson,medi_sommeil,medi_trouble,medi_vitamines)
trt_total = trt_total %>%
mutate(x = tolower(x))%>%
filter(x != c(""),!x %in% medi_total)
dat_test <- data.frame(x = trt_total)
med_nb = dat_test %>%
mutate(x = tolower(x)) %>%
filter(dat_test != "",!x %in% medi_total)%>%
gather(value = "Modalités") %>%
group_by(Modalités) %>%
summarise(n = n()) %>% arrange(desc(n))
med_nb = as.data.frame(med_nb)
med_nb
#write_xlsx(med_nb,"//172.27.137.244/g_boilay/alternance/export/med.xlsx")
#write.csv(med_nb,"//172.27.137.244/g_boilay/alternance/export/med.csv")
1 = MA
2 = MCI amnésique
3 = MCI autre
4= Démence mixte
5 = Démence vasculaire
6 = DFT
7 = Lewy
8 = Atrophie corticale postérieure
9 = Démence alcoolique
10 = Aphasie progressive primaire
11 = Trouble psychiatrique
12 = VIH
13 = NSP
14 = Autre
## integer(0)
Les variables que nous cherchons à expliquer sont l’abeta42 et le tau.
## [1] "Modèle"
## [1] " + abeta42t2"
## [1] "N = 1723"
## [1] " + glucose_jeun"
## [1] "N = 1502"
## [1] " + triglycerides"
## [1] "N = 1282"
## [1] " + cholesterol_total"
## [1] "N = 1281"
## [1] " + age"
## [1] "N = 1281"
## [1] " + sexe"
## [1] "N = 1281"
## [1] " + apoe.reg2"
## [1] "N = 1177"
## [1] " + ch"
## [1] "N = 1177"
## [1] " + mmse"
## [1] "N = 1099"
## [1] " + dia"
## [1] "N = 1099"
## [1] " + imc"
## [1] "N = 935"
| abeta42 faible (N=484) | abeta42 normal (N=451) | Total (N=935) | p value | |
|---|---|---|---|---|
| Age | < 0.001 | |||
| - Mean (SD) | 71.145 (8.236) | 67.945 (9.425) | 69.601 (8.968) | |
| - Range | 47.000 - 89.000 | 42.000 - 89.000 | 42.000 - 89.000 | |
| Sexe | 0.015 | |||
| - Femme | 280 (57.9%) | 225 (49.9%) | 505 (54.0%) | |
| - Homme | 204 (42.1%) | 226 (50.1%) | 430 (46.0%) | |
| Glucose | 0.793 | |||
| - Mean (SD) | 5.758 (1.608) | 5.788 (1.870) | 5.773 (1.738) | |
| - Range | 0.720 - 18.500 | 0.920 - 24.500 | 0.720 - 24.500 | |
| Triglycerides | 0.467 | |||
| - Mean (SD) | 0.950 (0.343) | 0.967 (0.363) | 0.958 (0.353) | |
| - Range | 0.380 - 2.020 | 0.150 - 2.020 | 0.150 - 2.020 | |
| Cholesterol | 0.006 | |||
| - Mean (SD) | 2.171 (0.514) | 2.079 (0.512) | 2.127 (0.514) | |
| - Range | 1.030 - 6.640 | 0.970 - 5.900 | 0.970 - 6.640 | |
| Cholesterol hdl | 0.143 | |||
| - Mean (SD) | 0.601 (0.168) | 0.585 (0.152) | 0.593 (0.161) | |
| - Range | 0.210 - 1.590 | 0.280 - 1.380 | 0.210 - 1.590 | |
| Cholesterol ldl | < 0.001 | |||
| - Mean (SD) | 1.374 (0.406) | 1.279 (0.383) | 1.328 (0.398) | |
| - Range | 0.360 - 2.970 | 0.380 - 2.630 | 0.360 - 2.970 | |
| Présence E4 | < 0.001 | |||
| - 0 | 192 (39.7%) | 331 (73.4%) | 523 (55.9%) | |
| - 1 | 220 (45.5%) | 113 (25.1%) | 333 (35.6%) | |
| - 2 | 72 (14.9%) | 7 (1.6%) | 79 (8.4%) | |
| MMSE | < 0.001 | |||
| - Mean (SD) | 21.045 (5.517) | 23.900 (4.410) | 22.422 (5.211) | |
| - Range | 4.000 - 30.000 | 6.000 - 30.000 | 4.000 - 30.000 | |
| Traitement cholesterol | 0.310 | |||
| - 0 | 406 (83.9%) | 389 (86.3%) | 795 (85.0%) | |
| - 1 | 78 (16.1%) | 62 (13.7%) | 140 (15.0%) | |
| Traitement Diabete | 0.190 | |||
| - 0 | 454 (93.8%) | 413 (91.6%) | 867 (92.7%) | |
| - 1 | 30 (6.2%) | 38 (8.4%) | 68 (7.3%) | |
| Niveau d’étude | 0.248 | |||
| - N-Miss | 31 | 36 | 67 | |
| - études supérieurs | 169 (37.3%) | 156 (37.6%) | 325 (37.4%) | |
| - niveau études intermédiaires | 152 (33.6%) | 157 (37.8%) | 309 (35.6%) | |
| - niveau études mineurs | 132 (29.1%) | 102 (24.6%) | 234 (27.0%) |
## [1] "N = 1062"
Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age, family = binomial(logit),
## data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5436 -1.1814 0.9169 1.1308 1.5433
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.500378 0.517826 -4.829 1.37e-06 ***
## glucose_jeun -0.027111 0.034891 -0.777 0.437
## age 0.039116 0.007082 5.523 3.33e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1439.5 on 1059 degrees of freedom
## AIC: 1445.5
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe, family = binomial(logit),
## data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.608 -1.177 0.871 1.117 1.647
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.212214 0.588858 -5.455 4.90e-08 ***
## glucose_jeun -0.009055 0.035625 -0.254 0.7994
## age 0.040430 0.007133 5.668 1.45e-08 ***
## sexe 0.337535 0.128259 2.632 0.0085 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1432.5 on 1058 degrees of freedom
## AIC: 1440.5
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3765 -0.9938 0.4435 1.0069 1.9476
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.961609 0.633271 -6.256 3.96e-10 ***
## glucose_jeun -0.024368 0.037950 -0.642 0.521
## age 0.043812 0.007623 5.747 9.08e-09 ***
## sexe 0.326927 0.136957 2.387 0.017 *
## apoe.reg2 1.254773 0.115784 10.837 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1292.8 on 1057 degrees of freedom
## AIC: 1302.8
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3826 -0.9898 0.4455 1.0034 1.9484
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.97917 0.64172 -6.201 5.62e-10 ***
## glucose_jeun -0.02507 0.04201 -0.597 0.5507
## age 0.04422 0.00773 5.720 1.07e-08 ***
## sexe 0.32720 0.13697 2.389 0.0169 *
## apoe.reg2 1.25621 0.11611 10.819 < 2e-16 ***
## ch -0.06154 0.19064 -0.323 0.7468
## dia 0.02249 0.27725 0.081 0.9354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1292.7 on 1055 degrees of freedom
## AIC: 1306.7
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3994 -0.9332 0.3529 0.9654 1.9361
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.193250 0.744224 -1.603 0.1089
## glucose_jeun -0.036122 0.042686 -0.846 0.3974
## age 0.043277 0.008012 5.402 6.60e-08 ***
## sexe 0.245278 0.141668 1.731 0.0834 .
## apoe.reg2 1.209507 0.118358 10.219 < 2e-16 ***
## ch -0.042302 0.195932 -0.216 0.8291
## dia 0.042077 0.285132 0.148 0.8827
## mmse -0.111050 0.014546 -7.634 2.27e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1228.1 on 1054 degrees of freedom
## AIC: 1244.1
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3836 -0.9303 0.3485 0.9616 2.0464
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.033108 0.866632 0.038 0.96953
## glucose_jeun -0.012802 0.043970 -0.291 0.77093
## age 0.041457 0.008081 5.130 2.90e-07 ***
## sexe 0.223409 0.142388 1.569 0.11664
## apoe.reg2 1.184760 0.119049 9.952 < 2e-16 ***
## ch 0.002593 0.197607 0.013 0.98953
## dia 0.062715 0.287094 0.218 0.82708
## mmse -0.111574 0.014579 -7.653 1.96e-14 ***
## imc -0.047291 0.017031 -2.777 0.00549 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1220.2 on 1053 degrees of freedom
## AIC: 1238.2
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| glucose_jeun | 0.97 | 0.91, 1.04 | 0.4 | 0.99 | 0.92, 1.06 | 0.8 | 0.98 | 0.91, 1.05 | 0.5 | 0.98 | 0.90, 1.06 | 0.6 | 0.96 | 0.89, 1.05 | 0.4 | 0.99 | 0.91, 1.08 | 0.8 |
| age | 1.04 | 1.03, 1.05 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 |
| sexe | 1.40 | 1.09, 1.80 | 0.008 | 1.39 | 1.06, 1.82 | 0.017 | 1.39 | 1.06, 1.82 | 0.017 | 1.28 | 0.97, 1.69 | 0.083 | 1.25 | 0.95, 1.65 | 0.12 | |||
| apoe.reg2 | 3.51 | 2.81, 4.42 | <0.001 | 3.51 | 2.81, 4.43 | <0.001 | 3.35 | 2.67, 4.24 | <0.001 | 3.27 | 2.60, 4.15 | <0.001 | ||||||
| ch | 0.94 | 0.65, 1.37 | 0.7 | 0.96 | 0.65, 1.41 | 0.8 | 1.00 | 0.68, 1.48 | >0.9 | |||||||||
| dia | 1.02 | 0.59, 1.76 | >0.9 | 1.04 | 0.59, 1.82 | 0.9 | 1.06 | 0.60, 1.87 | 0.8 | |||||||||
| mmse | 0.89 | 0.87, 0.92 | <0.001 | 0.89 | 0.87, 0.92 | <0.001 | ||||||||||||
| imc | 0.95 | 0.92, 0.99 | 0.005 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
## [1] "N = 962"
Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE +
IMC
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age, family = binomial(logit),
## data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5592 -1.1922 0.9114 1.1210 1.5407
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.605758 0.556204 -4.685 2.80e-06 ***
## triglycerides -0.070813 0.184389 -0.384 0.701
## age 0.039595 0.007484 5.291 1.22e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1302.6 on 959 degrees of freedom
## AIC: 1308.6
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe, family = binomial(logit),
## data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6275 -1.1824 0.8834 1.1120 1.5633
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.293534 0.615915 -5.347 8.92e-08 ***
## triglycerides -0.037037 0.185644 -0.200 0.84187
## age 0.041014 0.007544 5.437 5.43e-08 ***
## sexe 0.361829 0.132684 2.727 0.00639 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1295.1 on 958 degrees of freedom
## AIC: 1303.1
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4287 -0.9772 0.4346 0.9763 1.8920
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.226190 0.669437 -6.313 2.74e-10 ***
## triglycerides -0.036934 0.200268 -0.184 0.8537
## age 0.045165 0.008122 5.561 2.68e-08 ***
## sexe 0.348478 0.142805 2.440 0.0147 *
## apoe.reg2 1.330147 0.123662 10.756 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1155.6 on 957 degrees of freedom
## AIC: 1165.6
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4330 -0.9757 0.4301 0.9785 1.8863
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.210505 0.671931 -6.266 3.70e-10 ***
## triglycerides -0.025122 0.201053 -0.125 0.9006
## age 0.045092 0.008236 5.475 4.38e-08 ***
## sexe 0.339107 0.143498 2.363 0.0181 *
## apoe.reg2 1.328357 0.123814 10.729 < 2e-16 ***
## ch 0.054458 0.207306 0.263 0.7928
## dia -0.189006 0.273658 -0.691 0.4898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1155.1 on 955 degrees of freedom
## AIC: 1169.1
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4098 -0.9194 0.3757 0.9464 1.9758
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.516928 0.780623 -1.943 0.0520 .
## triglycerides -0.101392 0.205252 -0.494 0.6213
## age 0.044231 0.008526 5.188 2.13e-07 ***
## sexe 0.276219 0.147843 1.868 0.0617 .
## apoe.reg2 1.258855 0.125675 10.017 < 2e-16 ***
## ch 0.068487 0.212689 0.322 0.7474
## dia -0.199196 0.282210 -0.706 0.4803
## mmse -0.107754 0.015390 -7.001 2.53e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1101.1 on 954 degrees of freedom
## AIC: 1117.1
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4214 -0.9213 0.3779 0.9471 2.0710
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.400336 0.906199 -0.442 0.6587
## triglycerides -0.023539 0.208083 -0.113 0.9099
## age 0.043332 0.008592 5.043 4.58e-07 ***
## sexe 0.245164 0.148821 1.647 0.0995 .
## apoe.reg2 1.241490 0.126370 9.824 < 2e-16 ***
## ch 0.107645 0.214328 0.502 0.6155
## dia -0.133611 0.284301 -0.470 0.6384
## mmse -0.107439 0.015392 -6.980 2.95e-12 ***
## imc -0.043708 0.017980 -2.431 0.0151 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1095.1 on 953 degrees of freedom
## AIC: 1113.1
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| triglycerides | 0.93 | 0.65, 1.34 | 0.7 | 0.96 | 0.67, 1.39 | 0.8 | 0.96 | 0.65, 1.43 | 0.9 | 0.98 | 0.66, 1.45 | >0.9 | 0.90 | 0.60, 1.35 | 0.6 | 0.98 | 0.65, 1.47 | >0.9 |
| age | 1.04 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 |
| sexe | 1.44 | 1.11, 1.86 | 0.006 | 1.42 | 1.07, 1.88 | 0.015 | 1.40 | 1.06, 1.86 | 0.018 | 1.32 | 0.99, 1.76 | 0.062 | 1.28 | 0.95, 1.71 | 0.10 | |||
| apoe.reg2 | 3.78 | 2.98, 4.84 | <0.001 | 3.77 | 2.97, 4.83 | <0.001 | 3.52 | 2.76, 4.53 | <0.001 | 3.46 | 2.71, 4.45 | <0.001 | ||||||
| ch | 1.06 | 0.70, 1.59 | 0.8 | 1.07 | 0.71, 1.63 | 0.7 | 1.11 | 0.73, 1.70 | 0.6 | |||||||||
| dia | 0.83 | 0.48, 1.41 | 0.5 | 0.82 | 0.47, 1.42 | 0.5 | 0.87 | 0.50, 1.53 | 0.6 | |||||||||
| mmse | 0.90 | 0.87, 0.92 | <0.001 | 0.90 | 0.87, 0.93 | <0.001 | ||||||||||||
| imc | 0.96 | 0.92, 0.99 | 0.015 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
## [1] "N = 1006"
Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE +
IMC
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age, family = binomial(logit),
## data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9446 -1.1586 0.8581 1.1197 1.5276
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.723353 0.626945 -5.939 2.87e-09 ***
## cholesterol_total 0.393784 0.131439 2.996 0.00274 **
## age 0.042260 0.007404 5.708 1.14e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1354.4 on 1003 degrees of freedom
## AIC: 1360.4
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe, family = binomial(logit),
## data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.853 -1.164 0.839 1.123 1.543
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.996785 0.642066 -6.225 4.82e-10 ***
## cholesterol_total 0.302169 0.137823 2.192 0.0283 *
## age 0.042807 0.007427 5.763 8.25e-09 ***
## sexe 0.282006 0.136797 2.061 0.0393 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1350.1 on 1002 degrees of freedom
## AIC: 1358.1
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4789 -0.9833 0.4258 1.0000 1.8767
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.688035 0.688074 -6.813 9.54e-12 ***
## cholesterol_total 0.188765 0.142824 1.322 0.1863
## age 0.046406 0.007952 5.835 5.36e-09 ***
## sexe 0.308945 0.146143 2.114 0.0345 *
## apoe.reg2 1.279867 0.120011 10.665 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1215.2 on 1001 degrees of freedom
## AIC: 1225.2
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4799 -0.9833 0.4254 1.0008 1.8782
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.699349 0.691618 -6.795 1.09e-11 ***
## cholesterol_total 0.190582 0.144377 1.320 0.1868
## age 0.046448 0.008042 5.775 7.68e-09 ***
## sexe 0.310646 0.146485 2.121 0.0339 *
## apoe.reg2 1.280625 0.120094 10.664 < 2e-16 ***
## ch -0.011952 0.199579 -0.060 0.9522
## dia 0.043510 0.262723 0.166 0.8685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1215.2 on 999 degrees of freedom
## AIC: 1229.2
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5070 -0.9507 0.3601 0.9486 2.0180
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.204502 0.779108 -2.830 0.00466 **
## cholesterol_total 0.249671 0.148804 1.678 0.09338 .
## age 0.046152 0.008331 5.540 3.02e-08 ***
## sexe 0.224276 0.151445 1.481 0.13863
## apoe.reg2 1.208232 0.122048 9.900 < 2e-16 ***
## ch 0.018260 0.204781 0.089 0.92895
## dia 0.034869 0.270726 0.129 0.89752
## mmse -0.107932 0.015054 -7.170 7.53e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1158.6 on 998 degrees of freedom
## AIC: 1174.6
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4778 -0.9344 0.3494 0.9521 2.0426
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.065190 0.930610 -1.145 0.2524
## cholesterol_total 0.222673 0.149946 1.485 0.1375
## age 0.044793 0.008394 5.336 9.48e-08 ***
## sexe 0.201153 0.152256 1.321 0.1864
## apoe.reg2 1.191730 0.122564 9.723 < 2e-16 ***
## ch 0.048016 0.205996 0.233 0.8157
## dia 0.090926 0.273085 0.333 0.7392
## mmse -0.107735 0.015061 -7.153 8.48e-13 ***
## imc -0.038209 0.017098 -2.235 0.0254 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1153.6 on 997 degrees of freedom
## AIC: 1171.6
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| triglycerides | 0.93 | 0.65, 1.34 | 0.7 | 0.96 | 0.67, 1.39 | 0.8 | 0.96 | 0.65, 1.43 | 0.9 | 0.98 | 0.66, 1.45 | >0.9 | 0.90 | 0.60, 1.35 | 0.6 | 0.98 | 0.65, 1.47 | >0.9 |
| age | 1.04 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 |
| sexe | 1.44 | 1.11, 1.86 | 0.006 | 1.42 | 1.07, 1.88 | 0.015 | 1.40 | 1.06, 1.86 | 0.018 | 1.32 | 0.99, 1.76 | 0.062 | 1.28 | 0.95, 1.71 | 0.10 | |||
| apoe.reg2 | 3.78 | 2.98, 4.84 | <0.001 | 3.77 | 2.97, 4.83 | <0.001 | 3.52 | 2.76, 4.53 | <0.001 | 3.46 | 2.71, 4.45 | <0.001 | ||||||
| ch | 1.06 | 0.70, 1.59 | 0.8 | 1.07 | 0.71, 1.63 | 0.7 | 1.11 | 0.73, 1.70 | 0.6 | |||||||||
| dia | 0.83 | 0.48, 1.41 | 0.5 | 0.82 | 0.47, 1.42 | 0.5 | 0.87 | 0.50, 1.53 | 0.6 | |||||||||
| mmse | 0.90 | 0.87, 0.92 | <0.001 | 0.90 | 0.87, 0.93 | <0.001 | ||||||||||||
| imc | 0.96 | 0.92, 0.99 | 0.015 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
## [1] "Modèle"
## [1] " + abeta42_"
## [1] "N = 1721"
## [1] " + glucose_jeun"
## [1] "N = 1501"
## [1] " + triglycerides"
## [1] "N = 1281"
## [1] " + cholesterol_total"
## [1] "N = 1280"
## [1] " + age"
## [1] "N = 1280"
## [1] " + sexe"
## [1] "N = 1280"
## [1] " + apoe.reg2"
## [1] "N = 1176"
## [1] " + ch"
## [1] "N = 1176"
## [1] " + mmse"
## [1] "N = 1098"
## [1] " + imc"
## [1] "N = 934"
## [1] " + dia"
## [1] "N = 934"
## [1] "N = 1061"
Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2522 -0.7928 -0.2886 0.8518 5.5710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.321998 0.252683 1.274 0.20283
## glucose_jeun 0.048287 0.017553 2.751 0.00605 **
## age -0.008519 0.003435 -2.480 0.01329 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.012 on 1058 degrees of freedom
## Multiple R-squared: 0.01178, Adjusted R-squared: 0.009916
## F-statistic: 6.308 on 2 and 1058 DF, p-value: 0.001891
## Warning in abline(abeta42_glucose.mod, col = "red"): utilisation des deux
## premiers des 3 coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2430 -0.7944 -0.2981 0.8569 5.5714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.359078 0.284562 1.262 0.20728
## glucose_jeun 0.047319 0.017889 2.645 0.00829 **
## age -0.008575 0.003442 -2.491 0.01289 *
## sexe -0.018057 0.063628 -0.284 0.77663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.013 on 1057 degrees of freedom
## Multiple R-squared: 0.01186, Adjusted R-squared: 0.009055
## F-statistic: 4.229 on 3 and 1057 DF, p-value: 0.005545
confint(mod, 'sexe', level=0.95)
## 2.5 % 97.5 %
## sexe -0.1429078 0.1067938
summary(mod)$coefficients
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.359077530 0.284562175 1.2618597 0.207277663
## glucose_jeun 0.047318930 0.017889112 2.6451246 0.008287209
## age -0.008574568 0.003442012 -2.4911500 0.012885544
## sexe -0.018056997 0.063627600 -0.2837919 0.776625443
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2,
## data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2073 -0.7938 -0.3108 0.8399 5.5089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.394297 0.284458 1.386 0.16600
## glucose_jeun 0.048223 0.017860 2.700 0.00704 **
## age -0.008456 0.003436 -2.461 0.01401 *
## sexe -0.013350 0.063542 -0.210 0.83363
## apoe.reg2 -0.107640 0.048069 -2.239 0.02535 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1056 degrees of freedom
## Multiple R-squared: 0.01653, Adjusted R-squared: 0.0128
## F-statistic: 4.437 on 4 and 1056 DF, p-value: 0.001466
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3452 -0.7965 -0.2960 0.8373 5.4255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.366298 0.288365 1.270 0.20427
## glucose_jeun 0.055361 0.019554 2.831 0.00473 **
## age -0.008514 0.003486 -2.442 0.01476 *
## sexe -0.013709 0.063578 -0.216 0.82933
## apoe.reg2 -0.111397 0.048299 -2.306 0.02128 *
## ch 0.014411 0.088269 0.163 0.87034
## dia -0.119165 0.130848 -0.911 0.36266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1054 degrees of freedom
## Multiple R-squared: 0.0173, Adjusted R-squared: 0.01171
## F-statistic: 3.093 on 6 and 1054 DF, p-value: 0.005237
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3449 -0.8016 -0.2889 0.8364 5.4073
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.170117 0.331867 0.513 0.60834
## glucose_jeun 0.056086 0.019560 2.867 0.00422 **
## age -0.008275 0.003492 -2.370 0.01797 *
## sexe -0.007248 0.063796 -0.114 0.90956
## apoe.reg2 -0.103763 0.048711 -2.130 0.03339 *
## ch 0.011375 0.088288 0.129 0.89751
## dia -0.120472 0.130827 -0.921 0.35734
## mmse 0.007206 0.006037 1.194 0.23291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1053 degrees of freedom
## Multiple R-squared: 0.01863, Adjusted R-squared: 0.01211
## F-statistic: 2.856 on 7 and 1053 DF, p-value: 0.005901
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3739 -0.8026 -0.2823 0.8396 5.3762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.268034 0.388588 0.690 0.49049
## glucose_jeun 0.058026 0.019972 2.905 0.00374 **
## age -0.008446 0.003511 -2.406 0.01631 *
## sexe -0.009209 0.063947 -0.144 0.88552
## apoe.reg2 -0.106274 0.049003 -2.169 0.03033 *
## ch 0.014225 0.088516 0.161 0.87236
## dia -0.118092 0.130966 -0.902 0.36742
## mmse 0.007172 0.006040 1.187 0.23532
## imc -0.003708 0.007648 -0.485 0.62788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1052 degrees of freedom
## Multiple R-squared: 0.01885, Adjusted R-squared: 0.01139
## F-statistic: 2.526 on 8 and 1052 DF, p-value: 0.01005
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | 0.01 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 |
| [-0.05, 0.07],(0.74) | [-0.07, 0.11],(0.66) | [-0.07, 0.11],(0.70) | [-0.07, 0.12],(0.61) | [-0.07, 0.12],(0.66) | [-0.07, 0.12],(0.65) | |
| glucose_jeun | 0.09 ** | 0.08 ** | 0.09 ** | 0.10 ** | 0.10 ** | 0.10 ** |
| [0.02, 0.15],(0.01) | [0.02, 0.15],(0.01) | [0.02, 0.15],(0.01) | [0.03, 0.17],(0.00) | [0.03, 0.17],(0.00) | [0.03, 0.17],(0.00) | |
| age | -0.08 * | -0.08 * | -0.08 * | -0.08 * | -0.08 * | -0.08 * |
| [-0.14, -0.02],(0.01) | [-0.14, -0.02],(0.01) | [-0.14, -0.02],(0.01) | [-0.14, -0.02],(0.01) | [-0.14, -0.01],(0.02) | [-0.14, -0.01],(0.02) | |
| sexe | -0.02 | -0.01 | -0.01 | -0.01 | -0.01 | |
| [-0.14, 0.11],(0.78) | [-0.14, 0.11],(0.83) | [-0.14, 0.11],(0.83) | [-0.13, 0.12],(0.91) | [-0.13, 0.12],(0.89) | ||
| apoe.reg2 | -0.07 * | -0.07 * | -0.07 * | -0.07 * | ||
| [-0.13, -0.01],(0.03) | [-0.13, -0.01],(0.02) | [-0.13, -0.01],(0.03) | [-0.13, -0.01],(0.03) | |||
| ch | 0.01 | 0.01 | 0.01 | |||
| [-0.16, 0.19],(0.87) | [-0.16, 0.18],(0.90) | [-0.16, 0.19],(0.87) | ||||
| dia | -0.12 | -0.12 | -0.12 | |||
| [-0.38, 0.14],(0.36) | [-0.38, 0.14],(0.36) | [-0.38, 0.14],(0.37) | ||||
| mmse | 0.04 | 0.04 | ||||
| [-0.02, 0.10],(0.23) | [-0.02, 0.10],(0.24) | |||||
| imc | -0.02 | |||||
| [-0.08, 0.05],(0.63) | ||||||
| N | 1061 | 1061 | 1061 | 1061 | 1061 | 1061 |
| R2 | 0.01 | 0.01 | 0.02 | 0.02 | 0.02 | 0.02 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
## [1] "N = 961"
Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE +
IMC
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1979 -0.8162 -0.2727 0.8537 5.9907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.690215 0.273350 2.525 0.0117 *
## triglycerides -0.144098 0.092347 -1.560 0.1190
## age -0.007442 0.003659 -2.034 0.0422 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 958 degrees of freedom
## Multiple R-squared: 0.006705, Adjusted R-squared: 0.004631
## F-statistic: 3.233 on 2 and 958 DF, p-value: 0.03985
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2404 -0.8152 -0.2611 0.8476 5.9443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.835103 0.299127 2.792 0.00535 **
## triglycerides -0.151771 0.092551 -1.640 0.10136
## age -0.007674 0.003663 -2.095 0.03644 *
## sexe -0.078787 0.066129 -1.191 0.23379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 957 degrees of freedom
## Multiple R-squared: 0.008176, Adjusted R-squared: 0.005067
## F-statistic: 2.63 on 3 and 957 DF, p-value: 0.04898
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2,
## data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2866 -0.7963 -0.2712 0.8428 5.8990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.870179 0.299414 2.906 0.00374 **
## triglycerides -0.150982 0.092444 -1.633 0.10275
## age -0.007597 0.003659 -2.076 0.03816 *
## sexe -0.074218 0.066101 -1.123 0.26180
## apoe.reg2 -0.091341 0.050781 -1.799 0.07238 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.017 on 956 degrees of freedom
## Multiple R-squared: 0.01152, Adjusted R-squared: 0.007386
## F-statistic: 2.786 on 4 and 956 DF, p-value: 0.02557
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2788 -0.8147 -0.2696 0.8463 5.9044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.879069 0.300994 2.921 0.00358 **
## triglycerides -0.151329 0.092869 -1.629 0.10354
## age -0.007820 0.003717 -2.104 0.03563 *
## sexe -0.073304 0.066433 -1.103 0.27012
## apoe.reg2 -0.091221 0.050905 -1.792 0.07345 .
## ch 0.030683 0.095480 0.321 0.74801
## dia 0.014427 0.130298 0.111 0.91186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 954 degrees of freedom
## Multiple R-squared: 0.01166, Adjusted R-squared: 0.005441
## F-statistic: 1.875 on 6 and 954 DF, p-value: 0.08211
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2877 -0.8085 -0.2659 0.8394 5.9002
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.791314 0.347122 2.280 0.0228 *
## triglycerides -0.149548 0.092971 -1.609 0.1080
## age -0.007714 0.003724 -2.072 0.0386 *
## sexe -0.070977 0.066616 -1.065 0.2869
## apoe.reg2 -0.087190 0.051539 -1.692 0.0910 .
## ch 0.029565 0.095542 0.309 0.7571
## dia 0.014487 0.130349 0.111 0.9115
## mmse 0.003262 0.006420 0.508 0.6115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 953 degrees of freedom
## Multiple R-squared: 0.01192, Adjusted R-squared: 0.004667
## F-statistic: 1.643 on 7 and 953 DF, p-value: 0.1197
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2904 -0.8051 -0.2551 0.8408 5.8910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.909149 0.408958 2.223 0.0264 *
## triglycerides -0.141928 0.094049 -1.509 0.1316
## age -0.007864 0.003735 -2.105 0.0355 *
## sexe -0.074296 0.066918 -1.110 0.2672
## apoe.reg2 -0.089830 0.051784 -1.735 0.0831 .
## ch 0.032643 0.095744 0.341 0.7332
## dia 0.022714 0.131267 0.173 0.8627
## mmse 0.003270 0.006422 0.509 0.6108
## imc -0.004398 0.008062 -0.545 0.5855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 952 degrees of freedom
## Multiple R-squared: 0.01223, Adjusted R-squared: 0.003933
## F-statistic: 1.474 on 8 and 952 DF, p-value: 0.1625
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | 0.03 | 0.08 | 0.07 | 0.07 | 0.07 | 0.07 |
| [-0.03, 0.10],(0.31) | [-0.02, 0.17],(0.12) | [-0.02, 0.17],(0.13) | [-0.03, 0.17],(0.19) | [-0.04, 0.17],(0.20) | [-0.03, 0.17],(0.20) | |
| triglycerides | -0.05 | -0.05 | -0.05 | -0.05 | -0.05 | -0.05 |
| [-0.12, 0.01],(0.12) | [-0.12, 0.01],(0.10) | [-0.12, 0.01],(0.10) | [-0.12, 0.01],(0.10) | [-0.12, 0.01],(0.11) | [-0.12, 0.02],(0.13) | |
| age | -0.07 * | -0.07 * | -0.07 * | -0.07 * | -0.07 * | -0.07 * |
| [-0.13, -0.00],(0.04) | [-0.13, -0.00],(0.04) | [-0.13, -0.00],(0.04) | [-0.14, -0.00],(0.04) | [-0.13, -0.00],(0.04) | [-0.14, -0.00],(0.04) | |
| sexe | -0.08 | -0.07 | -0.07 | -0.07 | -0.07 | |
| [-0.21, 0.05],(0.23) | [-0.20, 0.06],(0.26) | [-0.20, 0.06],(0.27) | [-0.20, 0.06],(0.29) | [-0.21, 0.06],(0.27) | ||
| apoe.reg2 | -0.06 | -0.06 | -0.06 | -0.06 | ||
| [-0.12, 0.01],(0.07) | [-0.12, 0.01],(0.07) | [-0.12, 0.01],(0.09) | [-0.12, 0.01],(0.08) | |||
| ch | 0.03 | 0.03 | 0.03 | |||
| [-0.16, 0.22],(0.75) | [-0.16, 0.22],(0.76) | [-0.16, 0.22],(0.73) | ||||
| dia | 0.01 | 0.01 | 0.02 | |||
| [-0.24, 0.27],(0.91) | [-0.24, 0.27],(0.91) | [-0.23, 0.28],(0.86) | ||||
| mmse | 0.02 | 0.02 | ||||
| [-0.05, 0.08],(0.61) | [-0.05, 0.08],(0.61) | |||||
| imc | -0.02 | |||||
| [-0.09, 0.05],(0.59) | ||||||
| N | 961 | 961 | 961 | 961 | 961 | 961 |
| R2 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
## [1] "N = 1005"
Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE +
IMC
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1742 -0.8281 -0.2635 0.8471 6.0522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.649197 0.299908 2.165 0.0306 *
## cholesterol_total -0.059883 0.062887 -0.952 0.3412
## age -0.007015 0.003599 -1.949 0.0516 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 1002 degrees of freedom
## Multiple R-squared: 0.004292, Adjusted R-squared: 0.002304
## F-statistic: 2.159 on 2 and 1002 DF, p-value: 0.1159
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1985 -0.8230 -0.2632 0.8375 6.0252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.698118 0.307034 2.274 0.0232 *
## cholesterol_total -0.043910 0.066433 -0.661 0.5088
## age -0.007085 0.003601 -1.968 0.0494 *
## sexe -0.051062 0.068329 -0.747 0.4551
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 1001 degrees of freedom
## Multiple R-squared: 0.004847, Adjusted R-squared: 0.001865
## F-statistic: 1.625 on 3 and 1001 DF, p-value: 0.1819
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2,
## data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2517 -0.8074 -0.2616 0.8583 5.9719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.714809 0.306646 2.331 0.0199 *
## cholesterol_total -0.031217 0.066612 -0.469 0.6394
## age -0.006955 0.003596 -1.934 0.0533 .
## sexe -0.050057 0.068220 -0.734 0.4633
## apoe.reg2 -0.103362 0.050222 -2.058 0.0398 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.02 on 1000 degrees of freedom
## Multiple R-squared: 0.009044, Adjusted R-squared: 0.005081
## F-statistic: 2.282 on 4 and 1000 DF, p-value: 0.05878
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2472 -0.8070 -0.2575 0.8599 5.9748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.716796 0.308275 2.325 0.0203 *
## cholesterol_total -0.029337 0.067329 -0.436 0.6631
## age -0.007085 0.003643 -1.945 0.0521 .
## sexe -0.050225 0.068439 -0.734 0.4632
## apoe.reg2 -0.103588 0.050353 -2.057 0.0399 *
## ch 0.020709 0.092994 0.223 0.8238
## dia 0.003516 0.126131 0.028 0.9778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 998 degrees of freedom
## Multiple R-squared: 0.009097, Adjusted R-squared: 0.00314
## F-statistic: 1.527 on 6 and 998 DF, p-value: 0.1659
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2618 -0.8055 -0.2617 0.8473 5.9674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.584556 0.347038 1.684 0.0924 .
## cholesterol_total -0.032614 0.067455 -0.483 0.6289
## age -0.006939 0.003648 -1.902 0.0574 .
## sexe -0.045238 0.068713 -0.658 0.5105
## apoe.reg2 -0.096972 0.050987 -1.902 0.0575 .
## ch 0.018139 0.093060 0.195 0.8455
## dia 0.003948 0.126152 0.031 0.9750
## mmse 0.005270 0.006348 0.830 0.4066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 997 degrees of freedom
## Multiple R-squared: 0.009782, Adjusted R-squared: 0.00283
## F-statistic: 1.407 on 7 and 997 DF, p-value: 0.1987
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2681 -0.8062 -0.2525 0.8474 5.9504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.763616 0.420997 1.814 0.0700 .
## cholesterol_total -0.036553 0.067673 -0.540 0.5892
## age -0.007211 0.003666 -1.967 0.0495 *
## sexe -0.049148 0.068924 -0.713 0.4760
## apoe.reg2 -0.100368 0.051198 -1.960 0.0502 .
## ch 0.021676 0.093199 0.233 0.8161
## dia 0.014765 0.126997 0.116 0.9075
## mmse 0.005287 0.006350 0.833 0.4052
## imc -0.005825 0.007749 -0.752 0.4524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 996 degrees of freedom
## Multiple R-squared: 0.01034, Adjusted R-squared: 0.002394
## F-statistic: 1.301 on 8 and 996 DF, p-value: 0.2389
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | 0.03 | 0.06 | 0.06 | 0.06 | 0.05 | 0.05 |
| [-0.03, 0.10],(0.30) | [-0.03, 0.16],(0.21) | [-0.04, 0.15],(0.22) | [-0.04, 0.16],(0.27) | [-0.05, 0.15],(0.29) | [-0.05, 0.16],(0.28) | |
| cholesterol_total | -0.03 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 |
| [-0.09, 0.03],(0.34) | [-0.09, 0.04],(0.51) | [-0.08, 0.05],(0.64) | [-0.08, 0.05],(0.66) | [-0.09, 0.05],(0.63) | [-0.09, 0.05],(0.59) | |
| age | -0.06 | -0.06 * | -0.06 | -0.06 | -0.06 | -0.07 * |
| [-0.13, 0.00],(0.05) | [-0.13, -0.00],(0.05) | [-0.13, 0.00],(0.05) | [-0.13, 0.00],(0.05) | [-0.13, 0.00],(0.06) | [-0.13, -0.00],(0.05) | |
| sexe | -0.05 | -0.05 | -0.05 | -0.05 | -0.05 | |
| [-0.19, 0.08],(0.46) | [-0.18, 0.08],(0.46) | [-0.18, 0.08],(0.46) | [-0.18, 0.09],(0.51) | [-0.18, 0.09],(0.48) | ||
| apoe.reg2 | -0.07 * | -0.07 * | -0.06 | -0.06 | ||
| [-0.13, -0.00],(0.04) | [-0.13, -0.00],(0.04) | [-0.13, 0.00],(0.06) | [-0.13, 0.00],(0.05) | |||
| ch | 0.02 | 0.02 | 0.02 | |||
| [-0.16, 0.20],(0.82) | [-0.16, 0.20],(0.85) | [-0.16, 0.20],(0.82) | ||||
| dia | 0.00 | 0.00 | 0.01 | |||
| [-0.24, 0.25],(0.98) | [-0.24, 0.25],(0.98) | [-0.23, 0.26],(0.91) | ||||
| mmse | 0.03 | 0.03 | ||||
| [-0.04, 0.09],(0.41) | [-0.04, 0.09],(0.41) | |||||
| imc | -0.02 | |||||
| [-0.09, 0.04],(0.45) | ||||||
| N | 1005 | 1005 | 1005 | 1005 | 1005 | 1005 |
| R2 | 0.00 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
## [1] "Modèle"
## [1] " + taut2"
## [1] "N = 1723"
## [1] " + glucose_jeun"
## [1] "N = 1502"
## [1] " + triglycerides"
## [1] "N = 1282"
## [1] " + cholesterol_total"
## [1] "N = 1281"
## [1] " + age"
## [1] "N = 1281"
## [1] " + sexe"
## [1] "N = 1281"
## [1] " + apoe.reg2"
## [1] "N = 1177"
## [1] " + ch"
## [1] "N = 1177"
## [1] " + mmse"
## [1] "N = 1099"
## [1] " + dia"
## [1] "N = 1099"
## [1] " + imc"
## [1] "N = 935"
| FALSE (N=413) | TRUE (N=522) | Total (N=935) | p value | |
|---|---|---|---|---|
| Age | < 0.001 | |||
| - Mean (SD) | 67.172 (9.292) | 71.523 (8.218) | 69.601 (8.968) | |
| - Range | 42.000 - 88.000 | 49.000 - 89.000 | 42.000 - 89.000 | |
| Sexe | < 0.001 | |||
| - Femme | 196 (47.5%) | 309 (59.2%) | 505 (54.0%) | |
| - Homme | 217 (52.5%) | 213 (40.8%) | 430 (46.0%) | |
| Glucose | 0.761 | |||
| - Mean (SD) | 5.753 (1.721) | 5.788 (1.754) | 5.773 (1.738) | |
| - Range | 0.720 - 24.500 | 0.920 - 20.900 | 0.720 - 24.500 | |
| Triglycerides | 0.017 | |||
| - Mean (SD) | 0.989 (0.364) | 0.934 (0.343) | 0.958 (0.353) | |
| - Range | 0.150 - 2.020 | 0.340 - 2.020 | 0.150 - 2.020 | |
| Cholesterol | 0.066 | |||
| - Mean (SD) | 2.092 (0.516) | 2.154 (0.512) | 2.127 (0.514) | |
| - Range | 0.970 - 5.900 | 1.030 - 6.640 | 0.970 - 6.640 | |
| Cholesterol hdl | < 0.001 | |||
| - Mean (SD) | 0.572 (0.154) | 0.610 (0.165) | 0.593 (0.161) | |
| - Range | 0.220 - 1.590 | 0.210 - 1.100 | 0.210 - 1.590 | |
| Cholesterol ldl | 0.055 | |||
| - Mean (SD) | 1.300 (0.383) | 1.350 (0.408) | 1.328 (0.398) | |
| - Range | 0.480 - 2.970 | 0.360 - 2.730 | 0.360 - 2.970 | |
| Présence E4 | < 0.001 | |||
| - 0 | 291 (70.5%) | 232 (44.4%) | 523 (55.9%) | |
| - 1 | 103 (24.9%) | 230 (44.1%) | 333 (35.6%) | |
| - 2 | 19 (4.6%) | 60 (11.5%) | 79 (8.4%) | |
| MMSE | < 0.001 | |||
| - Mean (SD) | 23.913 (4.453) | 21.243 (5.462) | 22.422 (5.211) | |
| - Range | 8.000 - 30.000 | 4.000 - 30.000 | 4.000 - 30.000 | |
| Traitement cholesterol | 0.734 | |||
| - 0 | 353 (85.5%) | 442 (84.7%) | 795 (85.0%) | |
| - 1 | 60 (14.5%) | 80 (15.3%) | 140 (15.0%) | |
| Traitement Diabete | 0.619 | |||
| - 0 | 381 (92.3%) | 486 (93.1%) | 867 (92.7%) | |
| - 1 | 32 (7.7%) | 36 (6.9%) | 68 (7.3%) | |
| Niveau d’étude | 0.466 | |||
| - N-Miss | 29 | 38 | 67 | |
| - études supérieurs | 141 (36.7%) | 184 (38.0%) | 325 (37.4%) | |
| - niveau études intermédiaires | 145 (37.8%) | 164 (33.9%) | 309 (35.6%) | |
| - niveau études mineurs | 98 (25.5%) | 136 (28.1%) | 234 (27.0%) |
## [1] "N = 1062"
Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age, family = binomial(logit),
## data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7760 -1.1958 0.8034 1.0600 1.6739
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.675284 0.538412 -6.826 8.72e-12 ***
## glucose_jeun -0.036869 0.035453 -1.040 0.298
## age 0.059360 0.007439 7.980 1.47e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1390.0 on 1059 degrees of freedom
## AIC: 1396
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0253422 0.0087008 0.0719 8.722e-12 ***
## glucose_jeun 0.9638019 0.8989036 1.0341 0.2984
## age 1.0611570 1.0459901 1.0770 1.468e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe, family = binomial(logit),
## data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9032 -1.1755 0.7584 1.0404 1.8355
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.802182 0.621516 -7.727 1.10e-14 ***
## glucose_jeun -0.009463 0.036407 -0.260 0.795
## age 0.061940 0.007554 8.200 2.40e-16 ***
## sexe 0.518029 0.132239 3.917 8.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1374.5 on 1058 degrees of freedom
## AIC: 1382.5
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0082118 0.0023842 0.0273 1.105e-14 ***
## glucose_jeun 0.9905821 0.9225289 1.0652 0.7949
## age 1.0638989 1.0484712 1.0800 2.400e-16 ***
## sexe 1.6787148 1.2965126 2.1777 8.952e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0514 -1.0820 0.6300 0.9997 2.0329
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.298000 0.645185 -8.212 < 2e-16 ***
## glucose_jeun -0.018725 0.037419 -0.500 0.616782
## age 0.064048 0.007787 8.225 < 2e-16 ***
## sexe 0.508909 0.136188 3.737 0.000186 ***
## apoe.reg2 0.835986 0.109611 7.627 2.41e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1310.6 on 1057 degrees of freedom
## AIC: 1320.6
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0050016 0.0013833 0.0174 < 2.2e-16 ***
## glucose_jeun 0.9814492 0.9119392 1.0571 0.6167817
## age 1.0661432 1.0502179 1.0828 < 2.2e-16 ***
## sexe 1.6634760 1.2748621 2.1748 0.0001864 ***
## apoe.reg2 2.3070884 1.8666250 2.8695 2.405e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0772 -1.0711 0.6329 0.9878 2.0364
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.387699 0.655519 -8.219 < 2e-16 ***
## glucose_jeun -0.017048 0.041537 -0.410 0.681484
## age 0.065620 0.007919 8.287 < 2e-16 ***
## sexe 0.510101 0.136270 3.743 0.000182 ***
## apoe.reg2 0.840498 0.109982 7.642 2.14e-14 ***
## ch -0.222286 0.188180 -1.181 0.237508
## dia 0.015966 0.273484 0.058 0.953445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1309.2 on 1055 degrees of freedom
## AIC: 1323.2
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0045725 0.0012383 0.0162 < 2.2e-16 ***
## glucose_jeun 0.9830963 0.9061876 1.0676 0.6814844
## age 1.0678209 1.0516056 1.0848 < 2.2e-16 ***
## sexe 1.6654596 1.2761794 2.1778 0.0001816 ***
## apoe.reg2 2.3175199 1.8736955 2.8845 2.136e-14 ***
## ch 0.8006864 0.5538763 1.1593 0.2375080
## dia 1.0160943 0.5943009 1.7412 0.9534454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2921 -1.0301 0.5591 0.9620 2.0668
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.15206 0.74252 -4.245 2.19e-05 ***
## glucose_jeun -0.02655 0.04209 -0.631 0.52821
## age 0.06540 0.00812 8.054 8.03e-16 ***
## sexe 0.44616 0.13936 3.201 0.00137 **
## apoe.reg2 0.77796 0.11147 6.979 2.97e-12 ***
## ch -0.20651 0.19132 -1.079 0.28041
## dia 0.02985 0.27861 0.107 0.91468
## mmse -0.08995 0.01410 -6.377 1.80e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1265.1 on 1054 degrees of freedom
## AIC: 1281.1
##
## Number of Fisher Scoring iterations: 3
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0427641 0.0098501 0.1815 2.185e-05 ***
## glucose_jeun 0.9738012 0.8968111 1.0589 0.528212
## age 1.0675838 1.0509627 1.0850 8.033e-16 ***
## sexe 1.5623053 1.1896561 2.0550 0.001367 **
## apoe.reg2 2.1770350 1.7545592 2.7171 2.971e-12 ***
## ch 0.8134203 0.5592232 1.1850 0.280407
## dia 1.0302988 0.5968231 1.7841 0.914681
## mmse 0.9139769 0.8886699 0.9392 1.802e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3933 -0.9979 0.5032 0.9449 2.1521
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.058634 0.860332 -1.230 0.21851
## glucose_jeun 0.014078 0.044227 0.318 0.75024
## age 0.063439 0.008246 7.694 1.43e-14 ***
## sexe 0.415079 0.140989 2.944 0.00324 **
## apoe.reg2 0.734846 0.112953 6.506 7.73e-11 ***
## ch -0.137548 0.194421 -0.707 0.47927
## dia 0.067999 0.282772 0.240 0.80996
## mmse -0.092277 0.014268 -6.468 9.96e-11 ***
## imc -0.083022 0.017256 -4.811 1.50e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1240.9 on 1053 degrees of freedom
## AIC: 1258.9
##
## Number of Fisher Scoring iterations: 3
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.346929 0.063946 1.8707 0.218512
## glucose_jeun 1.014178 0.930789 1.1078 0.750244
## age 1.065495 1.048645 1.0831 1.429e-14 ***
## sexe 1.514491 1.149463 1.9983 0.003239 **
## apoe.reg2 2.085161 1.675472 2.6098 7.729e-11 ***
## ch 0.871493 0.595617 1.2776 0.479272
## dia 1.070364 0.614933 1.8685 0.809963
## mmse 0.911853 0.886304 0.9373 9.959e-11 ***
## imc 0.920331 0.889359 0.9517 1.500e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| glucose_jeun | 0.96 | 0.90, 1.03 | 0.3 | 0.99 | 0.92, 1.07 | 0.8 | 0.98 | 0.91, 1.06 | 0.6 | 0.98 | 0.91, 1.07 | 0.7 | 0.97 | 0.90, 1.06 | 0.5 | 1.01 | 0.93, 1.11 | 0.8 |
| age | 1.06 | 1.05, 1.08 | <0.001 | 1.06 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 |
| sexe | 1.68 | 1.30, 2.18 | <0.001 | 1.66 | 1.27, 2.17 | <0.001 | 1.67 | 1.28, 2.18 | <0.001 | 1.56 | 1.19, 2.05 | 0.001 | 1.51 | 1.15, 2.00 | 0.003 | |||
| apoe.reg2 | 2.31 | 1.87, 2.87 | <0.001 | 2.32 | 1.87, 2.88 | <0.001 | 2.18 | 1.75, 2.72 | <0.001 | 2.09 | 1.68, 2.61 | <0.001 | ||||||
| ch | 0.80 | 0.55, 1.16 | 0.2 | 0.81 | 0.56, 1.18 | 0.3 | 0.87 | 0.60, 1.28 | 0.5 | |||||||||
| dia | 1.02 | 0.59, 1.74 | >0.9 | 1.03 | 0.60, 1.78 | >0.9 | 1.07 | 0.61, 1.87 | 0.8 | |||||||||
| mmse | 0.91 | 0.89, 0.94 | <0.001 | 0.91 | 0.89, 0.94 | <0.001 | ||||||||||||
| imc | 0.92 | 0.89, 0.95 | <0.001 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
## [1] "N = 962"
Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE +
IMC
##
## Call:
## glm(formula = taut2 ~ triglycerides + age, family = binomial(logit),
## data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7970 -1.2131 0.7845 1.0419 1.7015
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.447982 0.574677 -6.000 1.97e-09 ***
## triglycerides -0.388536 0.189244 -2.053 0.0401 *
## age 0.058717 0.007851 7.479 7.50e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1253.0 on 959 degrees of freedom
## AIC: 1259
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.031810 0.010172 0.0970 1.975e-09 ***
## triglycerides 0.678049 0.467221 0.9818 0.04006 *
## age 1.060475 1.044485 1.0772 7.499e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe, family = binomial(logit),
## data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.935 -1.178 0.752 1.021 1.807
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.568139 0.647516 -7.055 1.73e-12 ***
## triglycerides -0.339848 0.191594 -1.774 0.0761 .
## age 0.061632 0.007991 7.713 1.23e-14 ***
## sexe 0.567849 0.137217 4.138 3.50e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1235.6 on 958 degrees of freedom
## AIC: 1243.6
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0103773 0.0028633 0.0363 1.728e-12 ***
## triglycerides 0.7118782 0.4883272 1.0357 0.0761 .
## age 1.0635707 1.0472623 1.0806 1.231e-14 ***
## sexe 1.7644671 1.3496249 2.3117 3.499e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0845 -1.0719 0.6276 0.9747 2.0244
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.142770 0.674758 -7.622 2.50e-14 ***
## triglycerides -0.367371 0.198484 -1.851 0.0642 .
## age 0.064078 0.008262 7.756 8.78e-15 ***
## sexe 0.561915 0.141717 3.965 7.34e-05 ***
## apoe.reg2 0.868569 0.116071 7.483 7.26e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1173.8 on 957 degrees of freedom
## AIC: 1183.8
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0058415 0.0015234 0.0215 2.505e-14 ***
## triglycerides 0.6925525 0.4684666 1.0208 0.06419 .
## age 1.0661754 1.0492939 1.0839 8.779e-15 ***
## sexe 1.7540276 1.3299148 2.3186 7.338e-05 ***
## apoe.reg2 2.3834970 1.9048714 3.0035 7.259e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1091 -1.0677 0.6217 0.9671 2.0283
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.221243 0.679373 -7.685 1.53e-14 ***
## triglycerides -0.375945 0.199391 -1.885 0.0594 .
## age 0.065659 0.008409 7.808 5.79e-15 ***
## sexe 0.564857 0.142488 3.964 7.36e-05 ***
## apoe.reg2 0.870242 0.116061 7.498 6.47e-14 ***
## ch -0.238033 0.203516 -1.170 0.2422
## dia 0.082530 0.268912 0.307 0.7589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1172.5 on 955 degrees of freedom
## AIC: 1186.5
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0054006 0.0013953 0.0201 1.525e-14 ***
## triglycerides 0.6866401 0.4636211 1.0138 0.05937 .
## age 1.0678624 1.0506600 1.0859 5.790e-15 ***
## sexe 1.7591956 1.3318415 2.3290 7.363e-05 ***
## apoe.reg2 2.3874891 1.9080557 3.0084 6.472e-14 ***
## ch 0.7881769 0.5292142 1.1766 0.24216
## dia 1.0860317 0.6421532 1.8489 0.75892
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3800 -1.0147 0.5313 0.9458 2.1547
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.72841 0.77960 -3.500 0.000466 ***
## triglycerides -0.46211 0.20339 -2.272 0.023087 *
## age 0.06562 0.00867 7.569 3.77e-14 ***
## sexe 0.51551 0.14618 3.527 0.000421 ***
## apoe.reg2 0.78521 0.11790 6.660 2.74e-11 ***
## ch -0.23256 0.20783 -1.119 0.263152
## dia 0.08998 0.27700 0.325 0.745312
## mmse -0.10112 0.01528 -6.617 3.65e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1124.3 on 954 degrees of freedom
## AIC: 1140.3
##
## Number of Fisher Scoring iterations: 3
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.065323 0.014017 0.2987 0.0004656 ***
## triglycerides 0.629954 0.421984 0.9374 0.0230867 *
## age 1.067822 1.050093 1.0864 3.771e-14 ***
## sexe 1.674497 1.258364 2.2327 0.0004210 ***
## apoe.reg2 2.192862 1.745586 2.7724 2.740e-11 ***
## ch 0.792504 0.527599 1.1931 0.2631522
## dia 1.094149 0.636707 1.8922 0.7453116
## mmse 0.903824 0.876684 0.9309 3.655e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4733 -0.9876 0.4984 0.9145 2.2043
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.637392 0.903813 -0.705 0.48067
## triglycerides -0.322749 0.207873 -1.553 0.12051
## age 0.065273 0.008837 7.386 1.51e-13 ***
## sexe 0.465527 0.148107 3.143 0.00167 **
## apoe.reg2 0.750311 0.119546 6.276 3.47e-10 ***
## ch -0.171779 0.210855 -0.815 0.41526
## dia 0.220226 0.282967 0.778 0.43641
## mmse -0.102425 0.015413 -6.645 3.03e-11 ***
## imc -0.083872 0.018261 -4.593 4.37e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1102.3 on 953 degrees of freedom
## AIC: 1120.3
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.528669 0.089739 3.1149 0.480670
## triglycerides 0.724156 0.481135 1.0878 0.120513
## age 1.067450 1.049385 1.0864 1.511e-13 ***
## sexe 1.592853 1.192279 2.1315 0.001671 **
## apoe.reg2 2.117658 1.680238 2.6858 3.467e-10 ***
## ch 0.842165 0.557440 1.2755 0.415257
## dia 1.246358 0.717662 2.1832 0.436408
## mmse 0.902646 0.875307 0.9299 3.027e-11 ***
## imc 0.919549 0.886811 0.9527 4.371e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| triglycerides | 0.68 | 0.47, 0.98 | 0.040 | 0.71 | 0.49, 1.04 | 0.076 | 0.69 | 0.47, 1.02 | 0.064 | 0.69 | 0.46, 1.01 | 0.059 | 0.63 | 0.42, 0.94 | 0.023 | 0.72 | 0.48, 1.09 | 0.12 |
| age | 1.06 | 1.04, 1.08 | <0.001 | 1.06 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.09 | <0.001 | 1.07 | 1.05, 1.09 | <0.001 | 1.07 | 1.05, 1.09 | <0.001 |
| sexe | 1.76 | 1.35, 2.31 | <0.001 | 1.75 | 1.33, 2.32 | <0.001 | 1.76 | 1.33, 2.33 | <0.001 | 1.67 | 1.26, 2.23 | <0.001 | 1.59 | 1.19, 2.13 | 0.002 | |||
| apoe.reg2 | 2.38 | 1.90, 3.00 | <0.001 | 2.39 | 1.91, 3.01 | <0.001 | 2.19 | 1.75, 2.77 | <0.001 | 2.12 | 1.68, 2.69 | <0.001 | ||||||
| ch | 0.79 | 0.53, 1.18 | 0.2 | 0.79 | 0.53, 1.19 | 0.3 | 0.84 | 0.56, 1.28 | 0.4 | |||||||||
| dia | 1.09 | 0.64, 1.85 | 0.8 | 1.09 | 0.64, 1.89 | 0.7 | 1.25 | 0.72, 2.18 | 0.4 | |||||||||
| mmse | 0.90 | 0.88, 0.93 | <0.001 | 0.90 | 0.88, 0.93 | <0.001 | ||||||||||||
| imc | 0.92 | 0.89, 0.95 | <0.001 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
## [1] "N = 1006"
Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE +
IMC
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age, family = binomial(logit),
## data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9232 -1.1860 0.7911 1.0419 1.6832
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.660714 0.647808 -7.195 6.27e-13 ***
## cholesterol_total 0.307506 0.133425 2.305 0.0212 *
## age 0.061145 0.007735 7.905 2.67e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1311.0 on 1003 degrees of freedom
## AIC: 1317
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0094597 0.0026040 0.0331 6.265e-13 ***
## cholesterol_total 1.3600295 1.0509789 1.7738 0.02118 *
## age 1.0630526 1.0472631 1.0795 2.671e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe, family = binomial(logit),
## data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.915 -1.171 0.747 1.033 1.789
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.213730 0.670521 -7.776 7.51e-15 ***
## cholesterol_total 0.141583 0.139622 1.014 0.310559
## age 0.062791 0.007821 8.029 9.86e-16 ***
## sexe 0.520688 0.140957 3.694 0.000221 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1297.2 on 1002 degrees of freedom
## AIC: 1305.2
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0054413 0.0014307 0.0199 7.507e-15 ***
## cholesterol_total 1.1520963 0.8789710 1.5207 0.3105593
## age 1.0648038 1.0488198 1.0815 9.863e-16 ***
## sexe 1.6831846 1.2780364 2.2215 0.0002208 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0700 -1.0748 0.6220 0.9793 1.9914
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.649346 0.694910 -8.130 4.31e-16 ***
## cholesterol_total 0.049765 0.141842 0.351 0.725702
## age 0.065037 0.008087 8.042 8.81e-16 ***
## sexe 0.542792 0.145418 3.733 0.000189 ***
## apoe.reg2 0.875663 0.114193 7.668 1.74e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1232.3 on 1001 degrees of freedom
## AIC: 1242.3
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.00351982 0.00088062 0.0135 4.307e-16 ***
## cholesterol_total 1.05102417 0.79646116 1.3921 0.7257016
## age 1.06719872 1.05065308 1.0845 8.813e-16 ***
## sexe 1.72080548 1.29536480 2.2914 0.0001895 ***
## apoe.reg2 2.40046625 1.92522567 3.0133 1.743e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1020 -1.0681 0.6216 0.9718 1.9898
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.713135 0.699717 -8.165 3.22e-16 ***
## cholesterol_total 0.029640 0.143704 0.206 0.836590
## age 0.066972 0.008216 8.152 3.59e-16 ***
## sexe 0.549691 0.145812 3.770 0.000163 ***
## apoe.reg2 0.880076 0.114262 7.702 1.34e-14 ***
## ch -0.297787 0.196708 -1.514 0.130062
## dia 0.057587 0.259896 0.222 0.824642
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1230.1 on 999 degrees of freedom
## AIC: 1244.1
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.00330230 0.00081814 0.0127 3.216e-16 ***
## cholesterol_total 1.03008375 0.77741091 1.3690 0.8365895
## age 1.06926569 1.05242906 1.0869 3.594e-16 ***
## sexe 1.73271711 1.30333006 2.3090 0.0001633 ***
## apoe.reg2 2.41108347 1.93345772 3.0270 1.337e-14 ***
## ch 0.74245915 0.50489384 1.0928 0.1300622
## dia 1.05927791 0.63691485 1.7694 0.8246417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3359 -1.0154 0.5441 0.9402 2.1084
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.495554 0.778888 -4.488 7.19e-06 ***
## cholesterol_total 0.074160 0.146956 0.505 0.61381
## age 0.067420 0.008462 7.968 1.61e-15 ***
## sexe 0.480575 0.149629 3.212 0.00132 **
## apoe.reg2 0.797070 0.115989 6.872 6.33e-12 ***
## ch -0.280416 0.200623 -1.398 0.16219
## dia 0.045243 0.266505 0.170 0.86520
## mmse -0.097057 0.014862 -6.531 6.55e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1183.5 on 998 degrees of freedom
## AIC: 1199.5
##
## Number of Fisher Scoring iterations: 3
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.0303320 0.0064902 0.1379 7.194e-06 ***
## cholesterol_total 1.0769794 0.8079718 1.4413 0.613811
## age 1.0697447 1.0524064 1.0879 1.615e-15 ***
## sexe 1.6170038 1.2068952 2.1705 0.001319 **
## apoe.reg2 2.2190299 1.7728729 2.7946 6.333e-12 ***
## ch 0.7554693 0.5098089 1.1205 0.162194
## dia 1.0462816 0.6210770 1.7706 0.865197
## mmse 0.9075046 0.8810193 0.9339 6.552e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4409 -0.9879 0.4925 0.9248 2.1827
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.170054 0.927097 -1.262 0.20693
## cholesterol_total 0.014869 0.149807 0.099 0.92094
## age 0.066031 0.008611 7.668 1.74e-14 ***
## sexe 0.443433 0.151478 2.927 0.00342 **
## apoe.reg2 0.764500 0.117424 6.511 7.49e-11 ***
## ch -0.232096 0.203499 -1.141 0.25407
## dia 0.160569 0.271672 0.591 0.55449
## mmse -0.098491 0.015002 -6.565 5.19e-11 ***
## imc -0.080280 0.017480 -4.593 4.38e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1161.5 on 997 degrees of freedom
## AIC: 1179.5
##
## Number of Fisher Scoring iterations: 4
## Attente de la réalisation du profilage...
## OR 2.5 % 97.5 % p
## (Intercept) 0.310350 0.050153 1.9073 0.206927
## cholesterol_total 1.014980 0.757085 1.3655 0.920935
## age 1.068259 1.050633 1.0867 1.742e-14 ***
## sexe 1.558046 1.158526 2.0987 0.003418 **
## apoe.reg2 2.147921 1.711151 2.7125 7.485e-11 ***
## ch 0.792870 0.532107 1.1828 0.254067
## dia 1.174179 0.690688 2.0095 0.554493
## mmse 0.906204 0.879506 0.9328 5.189e-11 ***
## imc 0.922858 0.891390 0.9547 4.377e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| glucose_jeun | 0.96 | 0.90, 1.03 | 0.3 | 0.99 | 0.92, 1.07 | 0.8 | 0.98 | 0.91, 1.06 | 0.6 | 0.98 | 0.91, 1.07 | 0.7 | 0.97 | 0.90, 1.06 | 0.5 | 1.01 | 0.93, 1.11 | 0.8 |
| age | 1.06 | 1.05, 1.08 | <0.001 | 1.06 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 |
| sexe | 1.68 | 1.30, 2.18 | <0.001 | 1.66 | 1.27, 2.17 | <0.001 | 1.67 | 1.28, 2.18 | <0.001 | 1.56 | 1.19, 2.05 | 0.001 | 1.51 | 1.15, 2.00 | 0.003 | |||
| apoe.reg2 | 2.31 | 1.87, 2.87 | <0.001 | 2.32 | 1.87, 2.88 | <0.001 | 2.18 | 1.75, 2.72 | <0.001 | 2.09 | 1.68, 2.61 | <0.001 | ||||||
| ch | 0.80 | 0.55, 1.16 | 0.2 | 0.81 | 0.56, 1.18 | 0.3 | 0.87 | 0.60, 1.28 | 0.5 | |||||||||
| dia | 1.02 | 0.59, 1.74 | >0.9 | 1.03 | 0.60, 1.78 | >0.9 | 1.07 | 0.61, 1.87 | 0.8 | |||||||||
| mmse | 0.91 | 0.89, 0.94 | <0.001 | 0.91 | 0.89, 0.94 | <0.001 | ||||||||||||
| imc | 0.92 | 0.89, 0.95 | <0.001 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
## [1] "Modèle"
## [1] " + tau_"
## [1] "N = 1722"
## [1] " + glucose_jeun"
## [1] "N = 1501"
## [1] " + triglycerides"
## [1] "N = 1281"
## [1] " + cholesterol_total"
## [1] "N = 1280"
## [1] " + age"
## [1] "N = 1280"
## [1] " + sexe"
## [1] "N = 1280"
## [1] " + apoe.reg2"
## [1] "N = 1176"
## [1] " + ch"
## [1] "N = 1176"
## [1] " + mmse"
## [1] "N = 1098"
## [1] " + dia"
## [1] "N = 1098"
## [1] " + imc"
## [1] "N = 934"
## [1] "N = 1061"
Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3354 -0.6850 -0.2529 0.4050 3.8102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.358451 0.237168 -1.511 0.1310
## glucose_jeun -0.013720 0.016476 -0.833 0.4052
## age 0.006052 0.003224 1.877 0.0608 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9501 on 1058 degrees of freedom
## Multiple R-squared: 0.003741, Adjusted R-squared: 0.001858
## F-statistic: 1.987 on 2 and 1058 DF, p-value: 0.1377
## Warning in abline(tau_glucose.mod, col = "red"): utilisation des deux premiers
## des 3 coefficients de régression
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3861 -0.6907 -0.2338 0.4097 3.8587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.607614 0.266579 -2.279 0.0228 *
## glucose_jeun -0.007215 0.016759 -0.431 0.6669
## age 0.006424 0.003224 1.992 0.0466 *
## sexe 0.121337 0.059607 2.036 0.0420 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9487 on 1057 degrees of freedom
## Multiple R-squared: 0.007632, Adjusted R-squared: 0.004815
## F-statistic: 2.71 on 3 and 1057 DF, p-value: 0.04396
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4206 -0.6885 -0.2479 0.4240 3.8212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.631759 0.266775 -2.368 0.0181 *
## glucose_jeun -0.007835 0.016750 -0.468 0.6400
## age 0.006343 0.003222 1.968 0.0493 *
## sexe 0.118110 0.059592 1.982 0.0477 *
## apoe.reg2 0.073792 0.045081 1.637 0.1020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9479 on 1056 degrees of freedom
## Multiple R-squared: 0.01014, Adjusted R-squared: 0.006394
## F-statistic: 2.705 on 4 and 1056 DF, p-value: 0.0292
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2 + ch +
## dia, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4198 -0.6862 -0.2462 0.4220 3.8228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.621889 0.270537 -2.299 0.0217 *
## glucose_jeun -0.009594 0.018346 -0.523 0.6011
## age 0.006298 0.003271 1.925 0.0544 .
## sexe 0.118187 0.059648 1.981 0.0478 *
## apoe.reg2 0.074488 0.045313 1.644 0.1005
## ch 0.005433 0.082812 0.066 0.9477
## dia 0.027684 0.122759 0.226 0.8216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9488 on 1054 degrees of freedom
## Multiple R-squared: 0.0102, Adjusted R-squared: 0.004566
## F-statistic: 1.81 on 6 and 1054 DF, p-value: 0.09388
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2 + ch +
## dia + mmse, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3997 -0.6782 -0.2538 0.4075 3.7472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.241859 0.310663 -0.779 0.4364
## glucose_jeun -0.010997 0.018310 -0.601 0.5482
## age 0.005833 0.003268 1.785 0.0746 .
## sexe 0.105672 0.059719 1.769 0.0771 .
## apoe.reg2 0.059700 0.045598 1.309 0.1907
## ch 0.011314 0.082647 0.137 0.8911
## dia 0.030215 0.122467 0.247 0.8052
## mmse -0.013959 0.005651 -2.470 0.0137 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9465 on 1053 degrees of freedom
## Multiple R-squared: 0.0159, Adjusted R-squared: 0.00936
## F-statistic: 2.431 on 7 and 1053 DF, p-value: 0.01793
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2 + ch +
## dia + mmse + imc, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4564 -0.6647 -0.2420 0.4117 3.7247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.213477 0.362795 0.588 0.5564
## glucose_jeun -0.001976 0.018646 -0.106 0.9156
## age 0.005037 0.003278 1.537 0.1247
## sexe 0.096553 0.059702 1.617 0.1061
## apoe.reg2 0.048024 0.045750 1.050 0.2941
## ch 0.024566 0.082640 0.297 0.7663
## dia 0.041281 0.122273 0.338 0.7357
## mmse -0.014116 0.005639 -2.503 0.0125 *
## imc -0.017243 0.007140 -2.415 0.0159 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9443 on 1052 degrees of freedom
## Multiple R-squared: 0.02133, Adjusted R-squared: 0.01388
## F-statistic: 2.866 on 8 and 1052 DF, p-value: 0.003709
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | -0.02 | -0.08 | -0.08 | -0.08 | -0.08 | -0.08 |
| [-0.08, 0.04],(0.54) | [-0.17, 0.00],(0.06) | [-0.16, 0.00],(0.06) | [-0.17, 0.01],(0.07) | [-0.17, 0.01],(0.09) | [-0.16, 0.01],(0.09) | |
| glucose_jeun | -0.02 | -0.01 | -0.01 | -0.02 | -0.02 | -0.00 |
| [-0.08, 0.03],(0.41) | [-0.07, 0.05],(0.67) | [-0.07, 0.04],(0.64) | [-0.08, 0.05],(0.60) | [-0.08, 0.04],(0.55) | [-0.07, 0.06],(0.92) | |
| age | 0.05 | 0.06 * | 0.06 * | 0.06 | 0.05 | 0.05 |
| [-0.00, 0.11],(0.06) | [0.00, 0.12],(0.05) | [0.00, 0.12],(0.05) | [-0.00, 0.12],(0.05) | [-0.01, 0.11],(0.07) | [-0.01, 0.10],(0.12) | |
| sexe | 0.12 * | 0.12 * | 0.12 * | 0.11 | 0.10 | |
| [0.00, 0.24],(0.04) | [0.00, 0.24],(0.05) | [0.00, 0.24],(0.05) | [-0.01, 0.22],(0.08) | [-0.02, 0.21],(0.11) | ||
| apoe.reg2 | 0.05 | 0.05 | 0.04 | 0.03 | ||
| [-0.01, 0.10],(0.10) | [-0.01, 0.11],(0.10) | [-0.02, 0.10],(0.19) | [-0.03, 0.09],(0.29) | |||
| ch | 0.01 | 0.01 | 0.02 | |||
| [-0.16, 0.17],(0.95) | [-0.15, 0.17],(0.89) | [-0.14, 0.19],(0.77) | ||||
| dia | 0.03 | 0.03 | 0.04 | |||
| [-0.21, 0.27],(0.82) | [-0.21, 0.27],(0.81) | [-0.20, 0.28],(0.74) | ||||
| mmse | -0.07 * | -0.07 * | ||||
| [-0.13, -0.01],(0.01) | [-0.13, -0.02],(0.01) | |||||
| imc | -0.07 * | |||||
| [-0.13, -0.01],(0.02) | ||||||
| N | 1061 | 1061 | 1061 | 1061 | 1061 | 1061 |
| R2 | 0.00 | 0.01 | 0.01 | 0.01 | 0.02 | 0.02 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
## [1] "N = 961"
Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE +
IMC
##
## Call:
## lm(formula = tau_ ~ triglycerides + age, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3294 -0.6933 -0.2524 0.4246 3.8024
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.290063 0.256226 -1.132 0.258
## triglycerides -0.011627 0.086562 -0.134 0.893
## age 0.004173 0.003430 1.217 0.224
##
## Residual standard error: 0.9543 on 958 degrees of freedom
## Multiple R-squared: 0.001567, Adjusted R-squared: -0.000517
## F-statistic: 0.752 on 2 and 958 DF, p-value: 0.4717
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3825 -0.6983 -0.2462 0.4234 3.8588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5121989 0.2800396 -1.829 0.0677 .
## triglycerides 0.0001375 0.0866452 0.002 0.9987
## age 0.0045282 0.0034296 1.320 0.1870
## sexe 0.1207923 0.0619095 1.951 0.0513 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9529 on 957 degrees of freedom
## Multiple R-squared: 0.005523, Adjusted R-squared: 0.002406
## F-statistic: 1.772 on 3 and 957 DF, p-value: 0.1509
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4180 -0.6987 -0.2488 0.4374 3.8198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5425169 0.2803782 -1.935 0.0533 .
## triglycerides -0.0005452 0.0865668 -0.006 0.9950
## age 0.0044611 0.0034267 1.302 0.1933
## sexe 0.1168435 0.0618985 1.888 0.0594 .
## apoe.reg2 0.0789522 0.0475524 1.660 0.0972 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.952 on 956 degrees of freedom
## Multiple R-squared: 0.008383, Adjusted R-squared: 0.004234
## F-statistic: 2.02 on 4 and 956 DF, p-value: 0.08955
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4195 -0.6996 -0.2475 0.4318 3.8215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.547546 0.281860 -1.943 0.0524 .
## triglycerides -0.003013 0.086965 -0.035 0.9724
## age 0.004521 0.003480 1.299 0.1942
## sexe 0.118480 0.062210 1.905 0.0571 .
## apoe.reg2 0.079812 0.047669 1.674 0.0944 .
## ch -0.017019 0.089410 -0.190 0.8491
## dia 0.038308 0.122015 0.314 0.7536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.953 on 954 degrees of freedom
## Multiple R-squared: 0.008506, Adjusted R-squared: 0.00227
## F-statistic: 1.364 on 6 and 954 DF, p-value: 0.2262
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4008 -0.6864 -0.2551 0.4471 3.7573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.198120 0.324303 -0.611 0.5414
## triglycerides -0.010103 0.086859 -0.116 0.9074
## age 0.004100 0.003479 1.178 0.2389
## sexe 0.109213 0.062237 1.755 0.0796 .
## apoe.reg2 0.063762 0.048150 1.324 0.1857
## ch -0.012565 0.089261 -0.141 0.8881
## dia 0.038073 0.121780 0.313 0.7546
## mmse -0.012989 0.005998 -2.166 0.0306 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9511 on 953 degrees of freedom
## Multiple R-squared: 0.01336, Adjusted R-squared: 0.006114
## F-statistic: 1.844 on 7 and 953 DF, p-value: 0.07578
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4449 -0.6810 -0.2503 0.4319 3.7275
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.172897 0.381455 0.453 0.6505
## triglycerides 0.013891 0.087724 0.158 0.8742
## age 0.003630 0.003484 1.042 0.2978
## sexe 0.098761 0.062418 1.582 0.1139
## apoe.reg2 0.055450 0.048302 1.148 0.2513
## ch -0.002872 0.089305 -0.032 0.9743
## dia 0.063978 0.122439 0.523 0.6014
## mmse -0.012965 0.005990 -2.164 0.0307 *
## imc -0.013847 0.007520 -1.841 0.0659 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9499 on 952 degrees of freedom
## Multiple R-squared: 0.01686, Adjusted R-squared: 0.008601
## F-statistic: 2.041 on 8 and 952 DF, p-value: 0.03906
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | -0.01 | -0.08 | -0.07 | -0.07 | -0.07 | -0.08 |
| [-0.07, 0.05],(0.73) | [-0.16, 0.01],(0.10) | [-0.16, 0.02],(0.10) | [-0.17, 0.02],(0.12) | [-0.17, 0.02],(0.14) | [-0.16, 0.01],(0.09) | |
| triglycerides | -0.00 | 0.00 | -0.00 | -0.00 | -0.00 | |
| [-0.06, 0.06],(0.89) | [-0.06, 0.06],(1.00) | [-0.06, 0.06],(0.99) | [-0.06, 0.06],(0.97) | [-0.06, 0.06],(0.91) | ||
| age | 0.04 | 0.04 | 0.04 | 0.04 | 0.04 | 0.05 |
| [-0.02, 0.10],(0.22) | [-0.02, 0.10],(0.19) | [-0.02, 0.10],(0.19) | [-0.02, 0.10],(0.19) | [-0.02, 0.10],(0.24) | [-0.01, 0.10],(0.12) | |
| sexe | 0.12 | 0.12 | 0.12 | 0.11 | 0.10 | |
| [-0.00, 0.24],(0.05) | [-0.00, 0.24],(0.06) | [-0.00, 0.24],(0.06) | [-0.01, 0.23],(0.08) | [-0.02, 0.21],(0.11) | ||
| apoe.reg2 | 0.05 | 0.05 | 0.04 | 0.03 | ||
| [-0.01, 0.11],(0.10) | [-0.01, 0.11],(0.09) | [-0.02, 0.10],(0.19) | [-0.03, 0.09],(0.29) | |||
| ch | -0.02 | -0.01 | 0.02 | |||
| [-0.19, 0.16],(0.85) | [-0.19, 0.16],(0.89) | [-0.14, 0.19],(0.77) | ||||
| dia | 0.04 | 0.04 | 0.04 | |||
| [-0.20, 0.28],(0.75) | [-0.20, 0.28],(0.75) | [-0.20, 0.28],(0.74) | ||||
| mmse | -0.07 * | -0.07 * | ||||
| [-0.13, -0.01],(0.03) | [-0.13, -0.02],(0.01) | |||||
| glucose_jeun | -0.00 | |||||
| [-0.07, 0.06],(0.92) | ||||||
| imc | -0.07 * | |||||
| [-0.13, -0.01],(0.02) | ||||||
| N | 961 | 961 | 961 | 961 | 961 | 1061 |
| R2 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 | 0.02 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
## [1] "N = 1005"
Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE +
IMC
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3196 -0.6846 -0.2534 0.4241 3.8190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.236187 0.278097 -0.849 0.396
## cholesterol_total -0.018758 0.058313 -0.322 0.748
## age 0.003721 0.003337 1.115 0.265
##
## Residual standard error: 0.947 on 1002 degrees of freedom
## Multiple R-squared: 0.001451, Adjusted R-squared: -0.0005425
## F-statistic: 0.7278 on 2 and 1002 DF, p-value: 0.4832
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3554 -0.6970 -0.2285 0.4346 3.8958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.367296 0.284120 -1.293 0.1964
## cholesterol_total -0.061565 0.061475 -1.001 0.3168
## age 0.003911 0.003332 1.174 0.2408
## sexe 0.136846 0.063229 2.164 0.0307 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9452 on 1001 degrees of freedom
## Multiple R-squared: 0.006101, Adjusted R-squared: 0.003123
## F-statistic: 2.048 on 3 and 1001 DF, p-value: 0.1055
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2,
## data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3842 -0.6914 -0.2438 0.4463 3.8587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.381137 0.283880 -1.343 0.1797
## cholesterol_total -0.072091 0.061667 -1.169 0.2427
## age 0.003803 0.003329 1.143 0.2535
## sexe 0.136012 0.063155 2.154 0.0315 *
## apoe.reg2 0.085714 0.046494 1.844 0.0655 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9441 on 1000 degrees of freedom
## Multiple R-squared: 0.009468, Adjusted R-squared: 0.005506
## F-statistic: 2.39 on 4 and 1000 DF, p-value: 0.04929
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3864 -0.6918 -0.2481 0.4432 3.8565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.383799 0.285389 -1.345 0.1790
## cholesterol_total -0.073568 0.062331 -1.180 0.2382
## age 0.003917 0.003372 1.162 0.2457
## sexe 0.136312 0.063358 2.151 0.0317 *
## apoe.reg2 0.086003 0.046615 1.845 0.0653 .
## ch -0.018846 0.086090 -0.219 0.8268
## dia 0.001231 0.116767 0.011 0.9916
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.945 on 998 degrees of freedom
## Multiple R-squared: 0.009516, Adjusted R-squared: 0.003561
## F-statistic: 1.598 on 6 and 998 DF, p-value: 0.1444
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3690 -0.6807 -0.2439 0.4400 3.7838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0527127 0.3205715 -0.164 0.8694
## cholesterol_total -0.0653630 0.0623110 -1.049 0.2944
## age 0.0035516 0.0033695 1.054 0.2921
## sexe 0.1238271 0.0634726 1.951 0.0514 .
## apoe.reg2 0.0694395 0.0470989 1.474 0.1407
## ch -0.0124093 0.0859627 -0.144 0.8852
## dia 0.0001498 0.1165310 0.001 0.9990
## mmse -0.0131953 0.0058641 -2.250 0.0247 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9431 on 997 degrees of freedom
## Multiple R-squared: 0.01452, Adjusted R-squared: 0.007602
## F-statistic: 2.099 on 7 and 997 DF, p-value: 0.04119
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4160 -0.6762 -0.2356 0.4233 3.7649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.380934 0.388242 0.981 0.3267
## cholesterol_total -0.074902 0.062408 -1.200 0.2303
## age 0.002891 0.003381 0.855 0.3927
## sexe 0.114359 0.063562 1.799 0.0723 .
## apoe.reg2 0.061217 0.047215 1.297 0.1951
## ch -0.003842 0.085948 -0.045 0.9644
## dia 0.026347 0.117116 0.225 0.8221
## mmse -0.013154 0.005856 -2.246 0.0249 *
## imc -0.014106 0.007146 -1.974 0.0487 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9417 on 996 degrees of freedom
## Multiple R-squared: 0.01836, Adjusted R-squared: 0.01048
## F-statistic: 2.329 on 8 and 996 DF, p-value: 0.01769
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | -0.02 | -0.09 * | -0.09 * | -0.09 | -0.08 | -0.08 |
| [-0.08, 0.04],(0.56) | [-0.18, -0.00],(0.05) | [-0.18, -0.00],(0.05) | [-0.18, 0.01],(0.07) | [-0.17, 0.01],(0.09) | [-0.17, 0.01],(0.09) | |
| cholesterol_total | -0.01 | -0.03 | -0.04 | -0.04 | -0.03 | -0.04 |
| [-0.07, 0.05],(0.75) | [-0.09, 0.03],(0.32) | [-0.10, 0.03],(0.24) | [-0.10, 0.03],(0.24) | [-0.10, 0.03],(0.29) | [-0.10, 0.02],(0.23) | |
| age | 0.03 | 0.04 | 0.03 | 0.04 | 0.03 | 0.03 |
| [-0.03, 0.09],(0.27) | [-0.02, 0.09],(0.24) | [-0.02, 0.09],(0.25) | [-0.02, 0.10],(0.25) | [-0.03, 0.09],(0.29) | [-0.03, 0.09],(0.39) | |
| sexe | 0.14 * | 0.14 * | 0.14 * | 0.12 | 0.11 | |
| [0.01, 0.26],(0.03) | [0.01, 0.26],(0.03) | [0.01, 0.26],(0.03) | [-0.00, 0.25],(0.05) | [-0.01, 0.24],(0.07) | ||
| apoe.reg2 | 0.06 | 0.06 | 0.04 | 0.04 | ||
| [-0.00, 0.11],(0.07) | [-0.00, 0.11],(0.07) | [-0.01, 0.10],(0.14) | [-0.02, 0.10],(0.20) | |||
| ch | -0.02 | -0.01 | -0.00 | |||
| [-0.19, 0.15],(0.83) | [-0.18, 0.16],(0.89) | [-0.17, 0.16],(0.96) | ||||
| dia | 0.00 | 0.00 | 0.03 | |||
| [-0.23, 0.23],(0.99) | [-0.23, 0.23],(1.00) | [-0.20, 0.26],(0.82) | ||||
| mmse | -0.07 * | -0.07 * | ||||
| [-0.13, -0.01],(0.02) | [-0.13, -0.01],(0.02) | |||||
| imc | -0.06 * | |||||
| [-0.12, -0.00],(0.05) | ||||||
| N | 1005 | 1005 | 1005 | 1005 | 1005 | 1005 |
| R2 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 | 0.02 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
# Export BDD avec scale variable
"data_export = data.frame(abeta42_continu = data2$abeta42_,
abeta_42_modalite = data2$abeta42t2,
tau_continu = data2$tau_,
tau_modalite = data2$taut2,
glucose = data2$glucose_jeun,
glucose_std = scale(data2$glucose_jeun),
triglycerides = data2$triglycerides,
triglycerides_std = scale(data2$triglycerides),
cholesterol_total = data2$cholesterol_total,
cholesterol_total_std = scale(data2$cholesterol_total),
cholesterol_hdl = data2$cholesterol_hdl,
cholesterol_hdl.std = scale(data2$cholesterol_hdl),
cholesterol_ldl = data2$cholesterol_ldl,
cholesterol_ldl.std = scale(data2$cholesterol_ldl),
education = data2$niveau_etude_reg,
age = data2$age,
sexe = data2$sexe_reg,
apoe = data2$apoe,
traitement_chol = data2$ch,
traitement_dia = data2$dia,
mmse = data2$mmse,
imc = data2$imc
)"
## [1] "data_export = data.frame(abeta42_continu = data2$abeta42_,\n abeta_42_modalite = data2$abeta42t2,\n tau_continu = data2$tau_,\n tau_modalite = data2$taut2,\n glucose = data2$glucose_jeun,\n glucose_std = scale(data2$glucose_jeun),\n triglycerides = data2$triglycerides,\n triglycerides_std = scale(data2$triglycerides),\n cholesterol_total = data2$cholesterol_total,\n cholesterol_total_std = scale(data2$cholesterol_total),\n cholesterol_hdl = data2$cholesterol_hdl,\n cholesterol_hdl.std = scale(data2$cholesterol_hdl),\n cholesterol_ldl = data2$cholesterol_ldl,\n cholesterol_ldl.std = scale(data2$cholesterol_ldl),\n education = data2$niveau_etude_reg,\n age = data2$age,\n sexe = data2$sexe_reg,\n apoe = data2$apoe,\n traitement_chol = data2$ch,\n traitement_dia = data2$dia,\n mmse = data2$mmse,\n imc = data2$imc\n )"
#write.csv(data_export,"//172.27.137.244/g_boilay/alternance/export/data_export.csv")